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Socio-Cognitive Scaffolding with Computer-Supported Collaboration Scripts: a Meta-Analysis
Open accessAbstract
Scripts for computer-supported collaborative learning (CSCL) offer socio-cognitive scaffolding for learners to engage in collaborative activities that are considered beneficial for learning. Yet, CSCL scripts are often criticized for hampering naturally emerging collaboration. Research on the effectiveness of CSCL scripts has shown divergent results. This article reports a meta-analysis about the effects of CSCL scripts on domain-specific knowledge and collaboration skills. Results indicate that CSCL scripts as a kind of socio-cognitive scaffolding can enhance learning outcomes substantially. Learning with CSCL scripts leads to a small positive effect on domain-specific knowledge ( d = 0.20) and a large positive effect on collaboration skills ( d = 0.95) compared to unstructured CSCL. Further analyses reveal that CSCL scripts are particularly effective for domain-specific learning when they prompt transactive activities (i.e., activities in which a learner’s reasoning builds on the contribution of a learning partner) and when they are combined with additional content-specific scaffolding (worked examples, concept maps, etc.). Future research on CSCL scripts should include measures of learners’ internal scripts (i.e., prior collaboration skills) and the transactivity of the actual learning process.
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“Scaffolding can target various aspects of cognitive learning processes, such as the activation of domain-knowledge (Sommerhoff et al., 2023) and the application of skills, such as collaboration (Vogel et al., 2017) and self-regulation (Azevedo et al., 2022).”
From Looking Beyond the Hype: Understanding the Effects of AI on Learning (2025) · cites this paper as Vogel et al., 2017
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“Content-specific support, in contrast, needs to be tailored to each new learning situation, which makes it costly to develop (e.g., Hannafin et al., 1999;Vogel et al., 2017).”
From Enhancing the Peer-Feedback Process Through Instructional Support: A Meta-Analysis (2025) · cites this paper as Vogel et al., 2017
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Metadata
- Authors
- Vogel, F., Wecker, C., Kollar, I., & Fischer, F.
- Affiliations
- Technical University of Munich (DE)
- Ludwig-Maximilians-Universität München (DE)
- University of Augsburg (DE)
- Corresponding author
- Vogel, Freydis
- Venue
- Educational Psychology Review
- Published
- 2016-03-23
- Volume
- 29
- Issue
- 3
- Pages
- 477-511
- ISSN
- 1040-726X (ISSN-L)
- 1573-336X
- OpenAlex
- W2305855202
- Cited by
- 278
- References
- 74
- Citations by year
- 2026: 30 · 2025: 33 · 2024: 30 · 2023: 24 · 2022: 37 · 2021: 43 · 2020: 26 · 2019: 30 · 2018: 13 · 2017: 7 · 2016: 5
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Educational Psychology Review (publishedVersion, cc-by)
- Repository@Nottingham (University of Nottingham) (publishedVersion, other-oa)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Nottingham ePrints (University of Nottingham) (submittedVersion, cc-by)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion, other-oa)
- OPUS (Augsburg University) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (DE) (FI 792/7-1)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- computer-supported collaborative learning, cooperative script, domain knowledge, collaboration skills, worked examples, concept maps, learning outcomes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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